Fleet Management for Autonomous Vehicles Using Multicommodity Coupled Flows in Time-Expanded Networks

VIPAFLEET is a framework to develop models and algorithms for managing a fleet of Individual Public Autonomous Vehicles (VIPA). We consider a homogeneous fleet of such vehicles distributed at specified stations in a closed site to supply internal transportation, where the vehicles can be used in different modes of circulation (tram mode, elevator mode, taxi mode). We treat in this paper a variant of the Online Pickup-and-Delivery Problem related to the taxi mode by means of multicommodity coupled flows in a time-expanded network and propose a corresponding integer linear programming formulation. This enables us to compute optimal offline solutions. However, to apply the well-known meta-strategy Replan to the online situation by solving a sequence of offline subproblems, the computation times turned out to be too long, so that we devise a heuristic approach h-Replan based on the flow formulation. Finally, we evaluate the performance of h-Replan in comparison with the optimal offline solution, both in terms of competitive analysis and computational experiments, showing that h-Replan computes reasonable solutions, so that it suits for the online situation

Reoptimisation strategies for dynamic vehicle routing problems with proximity-dependent nodes

Autonomous vehicles create new opportunities as well as new challenges to dynamic vehicle routing. The introduction of autonomous vehicles as information-collecting agents results in scenarios, where dynamic nodes are found by proximity. This paper presents a novel dynamic vehicle-routing problem variant with proximity-dependent nodes. Here, we introduced a novel variable, detectability, which determines whether a proximal dynamic node will be detected, based on the sight radius of the vehicle. The problem considered is motivated by autonomous weed-spraying vehicles in large agricultural operations. This work is generalisable to many other autonomous vehicle applications. The first step to crafting a solution approach for the problem is to decide when reoptimisation should be triggered. Two reoptimisation trigger strategies are considered—exogenous and endogenous. Computational experiments compared the strategies for both the classical dynamic vehicle routing problem as well as the introduced variant. Experiments used extensive standardised vehicle-routing problem benchmarks with varying degrees of dynamism and geographical node distributions. The results showed that for both the classical problem and the novel variant, an endogenous trigger strategy is better in most cases, while an exogenous trigger strategy is only suitable when both detectability and dynamism are low. Furthermore, the optimal level of detectability was shown to be dependent on the combination of trigger, degree of dynamism, and geographical node distribution, meaning practitioners may determine the required detectability based on the attributes of their specific problem

The interplay between land use, travel behaviour and attitudes: a quest for causality.

Governments increasingly embrace land-use policies to promote sustainable travel behaviour. However, the causality of this relationship, and in particular the role of travel-related attitudes, is not clear. This thesis takes a longitudinal approach and explores the directions of causality. It shows that the built environment influences travel behaviour and that travel-related attitudes play an important intervening role. Implications for land-use policies and alignment with accompanying measures are discussed.TRAIL Thesis Series no. T2021/18, the Netherlands Research School TRAILTransport and Plannin

Fleet management for autonomous vehicles: Online PDP under special constraints

International audienceThe VIPAFLEET project consists in developing models and algorithms for managing a fleet of Individual Public Autonomous Vehicles (VIPA). Hereby, we consider a fleet of cars distributed at specified stations in an industrial area to supply internal transportation, where the cars can be used in different modes of circulation (tram mode, elevator mode, taxi mode). One goal is to develop and implement suitable algorithms for each mode in order to satisfy all the requests under an economic point of view by minimizing the total tour length. The innovative idea and challenge of the project is to develop and install a dynamic fleet management system that allows the operator to switch between the different modes within the different periods of the day according to the dynamic transportation demands of the users. We model the underlying online transportation system and propose a corresponding fleet management framework, to handle modes, demands and commands. We consider two modes of circulation, tram and elevator mode, propose for each mode appropriate on-line algorithms and evaluate their performance, both in terms of competitive analysis and practical behavior

Fleet management for autonomous vehicles using flows in time-expanded networks

International audienceThe VIPAFLEET project aims at developing a framework to manage a fleet of Individual Public Autonomous Vehicles (VIPA). We consider a fleet of cars distributed at specified stations in an industrial area to supply internal transportation, where the cars can be used in different modes of circulation (tram mode, elevator mode, taxi mode). We treat in this paper the pickup and delivery problem related to the taxi mode by means of flows in time-expanded networks. This enables us to compute optimal offline solutions, to propose strategies for the online situation, and to evaluate their performance in comparison with the optimal offline solution

Fleet management for autonomous vehicles using flows in time-expanded networks

International audienceThe VIPAFLEET project aims at developing a framework to manage a fleet of Individual Public Autonomous Vehicles (VIPA). We consider a fleet of such cars distributed at specified stations in an industrial area to supply internal transportation , where the cars can be used in different modes of circulation (tram mode, elevator mode, taxi mode). We treat in this paper the pickup and delivery problem related to the taxi mode by means of flows in time expanded networks. We compute optimal offline solutions, propose a replan strategy for the online situation, and evaluate its performance in comparison with the optimal offline solution. Keywords: fleet management, offline and online pickup and delivery problem The project VIPAFLEET [5] aims at contributing to sustainable mobility through the development of innovative urban mobility solutions by means of fleets of Individual Public Autonomous Vehicles (VIPA) allowing passenger transport in closed sites like industrial areas, medical complexes or airports.

Dynamic Fleet Management for Autonomous Vehicles: Learning- and optimization-based strategies

Autonomous vehicles (AVs) have been heralded as the key to unlock a shared mobility future where transportation is more efficient, convenient, and cheaper. However, the AV utopia can only come to fruition if the majority of users trust that autonomous mobility-on-demand (AMoD) systems are on a par with owning a vehicle in terms of service quality. Once the perception of quality is highly subjective, we propose a more personalized approach to on-demand mobility, in which users are segmented into service quality classes. These classes comprise minimum requirements regarding responsiveness and privacy, allowing us to model a series of user profiles formalized using strict service quality contracts. By honoring these contracts, providers can build users' trust and gain their loyalty, which on a grander scheme can contribute to a faster transition to a shared mobility future.This thesis presents a series of strategies to guaranteeing service quality throughout operational scenarios arising in the timeline of AV technology deployment. First, a precondition to providing service quality in autonomous transportation is safety. During a transition phase to full automation, AV operation will likely be restricted to areas where safe operations are guaranteed, leading to the formation of hybrid street networks comprised of autonomous and non-autonomous vehicle zones. In this setting, meeting user service quality expectations is primarily a matter of coverage, once mobility services will have to access both AV-ready and not AV-ready areas. Accordingly, this thesis proposes solutions to overcome the challenges entailed by such a transition scenario, where infrastructures, regulatory measures, and AV technology are gradually evolving.Then, assuming that widespread automated driving is the new status quo, we set out to model rich autonomous transportation scenarios comprised of heterogeneous users and vehicles. Central to our analysis is finding an adequate tradeoff between fleet size and service quality. In traditional AMoD systems, providers can do only so much to prevent user dissatisfaction since, to some extent, this is a matter of having enough vehicles. When the demand outstrips the supply, users inevitably experience longer delays or even rejections, ultimately undermining trust in the service. However, these shortcomings may plague future transportation systems only if setting the fleet size and mix remains a strategic decision. In contrast to most related literature, this thesis investigates a disseminated AV ownership scenario, where ridesharing platforms can occasionally hire available privately-owned AVs on-demand. In this scenario, customers can simultaneously own and share AVs, a setup that better resembles the operation of today's transportation network companies (TNCs), which rely entirely on micro-operators. As a result, AMoD systems can increase and decrease vehicle supply in the short term, thus shifting fleet sizing to the operational planning level.Moreover, analogously to other transportation modes, we consider that the system must deal with a diversified user base with different service quality expectations. This setup allows providers greater leeway to explore requests' delay tolerances to design efficient routes. To balance user expectations and avoid an oversupply of vehicles, we propose a multi-objective matheuristic that dynamically hires third-party AVs to meet the demand. Our approach adds to recent literature by allowing providers to prioritize different customer segments, besides choosing the exact tradeoff between meeting each segment's needs and hiring extra vehicles. This way, when vehicles are lacking, the optimization process can steer the ride-matching solution towards addressing user requests in order of importance (e.g., most lucrative first). To make the most of currently working vehicles, we also design a repositioning algorithm that fixes supply and demand imbalances using users' service level violations as stimuli.Further, to enable anticipatory decision making, this thesis incorporates the stochastic information surrounding both privately-owned AV supply and heterogeneous passenger demand in the fleet management process. We propose a learning-based optimization approach that uses the underlying assignment problem's dual variables to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. In turn, these approximations are used in the optimization problem's objective function to weigh the downstream impact of dispatching, rebalancing, and occasionally hiring vehicles. By harnessing the historical knowledge regarding both demand and supply patterns, we show that AMoD providers are substantially better equipped to meet user needs without necessarily having to own large AV fleets.Typically, learning-based fleet management strategies end up reinforcing biases present in the demand data, therefore frequently moving towards cities' most affluent and densely populated areas, where alternative mobility choices already abound. Although lucrative for providers, this fleet management strategy runs counter to a broader city goal of equitably distributing accessibility across all regions and population demographics. To counterbalance the demand biases, we investigate the extent to which fare subsidization policies can drive the learning process towards sending vehicles to targeted regions where accessibility is lacking. Our results suggest that by using an adequate scheme of incentives, policymakers can orchestrate transportation providers to diminish the insidious effects of ``cream-skimming'' practices, thus using AVs in favor of mobility equity.Lastly, once we have designed strategies that balance the goals of cities, independent owners, fleet owners, and users, we focus on a different approach to maximizing fleet productivity in urban environments. No matter how efficient a fleet optimization method can be, by limiting AVs to service a single commodity type (i.e., people), fleet utilization and consequently profits are bounded by passenger demand patterns. As autonomous technology evolves, however, new opportunities to improve asset utilization arise. We end this thesis with a model for a versatile transportation system where mixed-purpose compartmentalized AVs can address both passengers and goods simultaneously. With the growth of e-commerce and same-day deliveries, our approach provides a starting point to study more flexible short-haul integration systems to consolidate passenger and freight flows.TRAIL Thesis Series no. T2021/12, the Netherlands TRAIL Research SchoolTransport Engineering and Logistic